Nathan Faber
Verified Expert in Engineering
Optimization Developer
Santa Rosa Beach, FL, United States
Toptal member since February 12, 2021
Nathan is a problem solver at heart, having worked as a chemical engineer, financial data scientist, and portfolio manager. He loves diving into messy datasets to uncover patterns and leverage the data to benefit the business. His quantitative and qualitative analysis skills in machine learning, visualization, and model creation, paired with his attention to detail and development experience in various programming languages, make him an ideal candidate for tackling any project.
Portfolio
Experience
- Quantitative Finance - 13 years
- Optimization - 13 years
- Python - 13 years
- Pandas - 12 years
- Machine Learning - 11 years
- Quantitative Research - 10 years
- Risk Models - 9 years
- SQL - 6 years
Preferred Environment
Python, SciPy, Pandas, Slack, NumPy, Excel VBA, C++, SQL, Apache
The most amazing...
...project I've developed is a flexible option strategy backtesting engine to create absolute return and risk management financial strategies.
Work Experience
Machine Learning Consultant
Self-employed
- Developed a comprehensive software package with one other engineer for a DARPA-funded client to use RL for training agents.
- Applied various RL algorithms (A2C, A3C, and PPO) to problems in robotics and control.
- Created a comprehensive rating model for a startup in the venture capital space. The model served as the basis for the launch of an early-stage venture fund.
Quantitative Researcher
One River Asset Management
- Researched tactical signals and monetization techniques for an equity-focused tail risk strategy, with the goal of increasing the capacity and complementing existing strategies within the firm’s flagship tail risk fund.
- Developed systematic relative volatility strategies within equities, commodities, fixed income, and FX, with a focus on non-equity volatility RV.
- Implemented trades throughout the day through Bloomberg's EMSX, OVML, FXEM, and direct chats with dealers for strategies using a variety of futures, options, and volatility swaps. Oversaw the trading for $20 million of volatility RV strategies that I manage.
Data Scientist
Private Wealth Management Firm
- Developed a financial forecasting engine in Python for existing and new private investments.
- Created a web app in JavaScript/Django to interact with user inputs and display model outputs.
- Launched a prototype app using cloud services for database management and user testing.
Vice President | Portfolio Manager
Newfound Research
- Developed, backtested, and operated quantitative, rules-based equity, futures, and options strategies focusing on prudent risk management. Managed over $1.1 billion in the strategy suite.
- Utilized techniques such as regression (linear, nonlinear, and nonparametric), clustering, cross-validation, PCA, machine learning (random forests, genetic algorithms), and data visualization.
- Created and optimized intuitive models for complex market behavior based on financial and behavioral economic theory to add value and expand upon the firm's existing algorithms.
- Utilized Python heavily, including Pandas, NumPy, SciPy, Scikit-learn, and Seaborn, to solve problems and improve task efficiency. Focused on test-driven development and used Github for version control.
- Explained market trends and specific strategy attribution to clients. Wrote market and strategy commentaries, recorded videos, and gave webinars to actively address client concerns and facilitate sales.
- Constructed portfolios with equities, fixed income, alternatives, and derivatives using various approaches such as target volatility, long and short risk parity, and smart beta.
- Applied hidden Markov models for market states in which different investment factors outperform. Calibrated arbitrage-free volatility surfaces using stochastic volatility-inspired models. Adapted Monte Carlo methods to simulate risk more accurately.
Process Engineer
AECOM
- Developed in-house software for process simulation and optimization, equipment sizing, and economic analysis.
- Replicated research from published papers and patents and evaluated the economic feasibility of the process.
- Managed economic and process-based studies within a larger group.
Experience
Options Backtesting Engine
End-to-end Reinforcement Learning (RL) Trainer
Deterministic Finance Forecasting Engine
I conducted end-to-end development from user management, user interface with graphical dashboards, data ingestion, financial modeling, user experience, database management, and DevOps.
Education
Master's Degree in Computational Finance
Carnegie Mellon University - Pittsburgh, PA, USA
Bachelor's Degree in Chemical Engineering
Case Western Reserve University - Cleveland, OH, USA
Skills
Libraries/APIs
SciPy, Pandas, NumPy, Scikit-learn, PyTorch, XGBoost, TensorFlow
Tools
Slack, MATLAB, OpenAI Gym, StatsModels, ARIMA, Bloomberg, Apache, Amazon SageMaker
Languages
Python, Excel VBA, Octave, C++, SQL, R, Visual Basic, JavaScript
Paradigms
Quantitative Research, ETL
Storage
Databases, PostgreSQL, Amazon S3 (AWS S3)
Platforms
Amazon Web Services (AWS), Google Cloud Platform (GCP), Vertex AI, Amazon EC2
Frameworks
Django
Other
Statistics, Finance, Numerical Methods, Optimization, Machine Learning, Risk Models, Forecasting, Monte Carlo Simulations, Chemical Engineering, Mathematical Modeling, Modeling, Data Science, Quantitative Finance, Quantitative Modeling, Artificial Intelligence (AI), Statistical Analysis, Data Analytics, Data Reporting, Natural Language Processing (NLP), Data Engineering, Data Analysis, Regression Modeling, Regression, Trading, Backtesting Trading Strategies, Data Modeling, Mathematical Finance, Model Validation, Statistical Modeling, Data Scientist, Time Series Forecasting, Probabilistic Modeling, Linear Regression, Feature Engineering, Time Series, Time Series Analysis, Predictive Modeling, Classification, Industry.Derivatives, Capital Structure & Products.Equity Derivatives, Backtesting, Investment & Trading Advisory.Options, Options, Equity Derivatives, Derivatives, Large Language Models (LLMs), Deep Learning, Machine Learning Operations (MLOps), Recurrent Neural Networks (RNNs), Neural Networks, Business Analysis, Financial Modeling, Currency Exchange, Fintech, Stock Market, Stock Trading, Stock Analysis, Performance Optimization, Financial Forecasting, A/B Testing, ETL Pipelines, AI Model Training, Logistics & Supply Chain, Logistic Regression, Recommendation Systems, AI Agents, Prompt Engineering, Algorithms, Economic Analysis, Deep Neural Networks (DNNs), Robotics, Deep Reinforcement Learning, Reinforcement Learning, API Integration, Generative Pre-trained Transformers (GPT), GPU Computing, Cloud Architecture, APIs, Bots, Cash Flow Forecasting, Normalization, Scientific Computing
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